PubGraph

PubGraph: visualizing results from PubMed

PubGraph is a visual interface to PubMed --
a way to get perspective on the biomedical research literature.

Users provide a set of PubMed queries as input.
PubGraph summarizes the associations in the results from PubMed as a graph:

Nodes of the resulting graph represent terms (queries)

Edges represent associations among terms.

All nodes and edges are clickable -- giving selective access to PubMed.

Different association measures are displayed in the graph,
permitting patterns in the literature to be seen.

Input

To use PubGraph, please go to the
Basic Query
interface and provide a set of PubMed queries that look like this:

In these example queries,
[tiab] (title and abstract)
[MH] (keyword, from the MeSH list of keywords)
are
PubMed search field tags.
These tell PubMed to search for documents in specific ways.
The PubMed online help
gives more ideas about how to develop powerful PubMed queries.
By choosing queries well we can get better perspective.

(This is an inactive example window. To use PubGraph, please enter queries on the
Basic Query page.)

Each of these five lines in this text area specifies a single PubMed query.
You can specify as many queries as you like, up to 100; PubGraph limits the number of queries to 100 since
beyond that the amount of information usually obscures any pattern -- the trees become a forest.

Output

PubGraph asks PubMed to find all relevant publications for these queries, and displays
a result page
that includes a graph representation:

Each node of this graph represents a PubMed query.

Each edge between nodes that have queries X and Y
represents the relationship between X and Y.

Each node is labeled with a number in parentheses -- this is the number of hits (publications)
that PubMed has found for that query.
So for example PubMed finds 59645 publications with the keyword dopamine.

Each edge is labeled with two numbers.
If the nodes have queries X and Y,
the top number is the number of hits for (X AND Y),
and the bottom number is the number of hits for (X OR Y).
The ratio of these numbers is called the
Jaccard co-occurrence index;
it measures the degree to which the two queries coincide among all publications.

For example, among the 59645 publications about dopamine
and the 66148 publications about stress,
492 are about (dopamine AND stress),
and 125301 are about (dopamine OR stress).
The Jaccard index is 492/125301 = 0.0039 = exp(-5.5).

The result page also includes a heatmap, showing hierarchical clusters of queries reflecting their
degree of association:

We can see that the "sleep disorders" and "stress, psychological" queries are clustered;
their results have high Jaccard coefficient, which is a commonly-used measure of co-occurrence in a set of documents.

PubGraph also provides the results in a similar, but clickable, table:

Tailoring the Output

PubGraph has many features for tailoring the output to provide different perspectives.

For example you can produce graphs with alternative colors for the nodes.
This is done by optionally start each line with a Group ID,
a single digit 0, 1, ..., 9:

In these example queries,
the initial Group ID digit specifies a node style.
You can specify that nodes in Group 2 are yellow circles, nodes in Group 4 are cyan boxes, and so on.
(Having no initial ID is completely equivalent to having ID 0.)

(This is an inactive example window. To use PubGraph, please enter queries on the
Basic Query page.)

"Quick tours" about specific kinds of queries are available at:
Searching PubMed.

Philosophy

PubGraph has been designed to give
an interesting high-level overview of
the biomedical research literature.
There are many possible uses --
gaining perspective on the state of a field,
automatically obtaining ontology-like models of
relationships between specified topics that highlight factors of interest,
finding patterns in the structure, history, evolution, and impact of research fields.
Achieving a good balance has required several innovations
in the modeling and visualization of associations.